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| Author(s): |
Luan Soares Oliveira
Total Authors: 1
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| Document type: | Master's Dissertation |
| Press: | São Carlos. |
| Institution: | Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) |
| Defense date: | 2015-08-18 |
| Examining board members: |
Gustavo Enrique de Almeida Prado Alves Batista;
Heloisa de Arruda Camargo;
André Carlos Ponce de Leon Ferreira de Carvalho
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| Advisor: | Gustavo Enrique de Almeida Prado Alves Batista |
| Abstract | |
Learning concepts from data streams differs significantly from traditional batch learning. In batch learning there is an implicit assumption that the concept to be learned is static and does not evolve significantly over time. On the other hand, in data stream learning the concepts to be learned may evolve over time. This evolution is called concept drift, and makes the creation of a fixed training set be no longer applicable. Incremental learning paradigm is a promising approach for learning in a data stream setting. However, in the presence of concept drifts, out dated concepts can cause misclassifications. Several incremental Gaussian mixture models methods have been proposed in the literature, but these algorithms lack an explicit policy to discard outdated concepts. In this work, a new incremental algorithm for data stream with concept drifts based on Gaussian Mixture Models is proposed. The proposed methodis compared to various algorithms widely used in the literature, and the results show that it is competitive with them invarious scenarios, overcoming them in some cases. (AU) | |
| FAPESP's process: | 13/16130-0 - Data Stream Classification with Incremental Algorithms applied to Intelligent Sensors |
| Grantee: | Luan Soares Oliveira |
| Support Opportunities: | Scholarships in Brazil - Master |